Differential patterns of fish sensitization in Asian populations: implication for precision diagnosis
Bibliographic record
Abstract
Background The current diagnostics of fish allergy lack sufficient accuracy such that more reliable tests such as component-resolved diagnosis (CRD) are urgently needed. This study aimed at identifying fish allergens of salmon and grass carp and evaluating the sensitization pattern towards the identified allergens in fish allergic subjects from two distinct populations in Asia. Methods One hundred and three fish allergic subjects were recruited from Hong Kong (67 subjects) and Japan (46 subjects). Western blot and mass spectrometry were used to identify allergens from salmon and grass carp. Fish allergens were purified and tested against 96 sera on ELISA to analyze patients’ sensitization pattern. The protein profiles of salmon meat prepared under different cooking methods until core temperature reached 80°C were evaluated by SDS-PAGE and mass spectrometry. Results Three common allergens between salmon and grass carp, namely enolase, glycerldehyde-3-phosphate dehydrogenase (GAPDH) and parvalbumin, and two salmon-specific allergens collagen and aldolase were identified. Parvalbumin was the major allergen for both fishes showing an overall sensitization rate of 74.7%, followed by collagen (38.9%), aldolase (38.5%) and enolase (17.8%). Japanese subjects showed more diverse allergen sensitization pattern and more frequent IgE-binding to heat-labile salmon allergens. Compared with steaming and boiling, cooking by baking and frying retained more fish proteins inclusive of heat-labile allergens. Conclusions Fish allergic patients from different Asian populations show varying fish allergen sensitization profiles. The relevant extracts and components for diagnosis are population-dependent but parvalbumin and collagen are important biomarkers. Cooking methods modify allergen composition of salmon and appear to influence patients’ allergic manifestations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".